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To enhance the power generation efficiency of the photovoltaic system, it is necessary to ensure that it can operate stably at the global maximum power point (MPP). This paper presents a study on the maximum power point tracking(MPPT) technology of photovoltaic system and a new MPPT technique based on the improved multi‐subgroup parallel optimization algorithm with shuffled frog leaping algorithm (IMSPO‐SFLA) is proposed, which combines the improved multi‐subgroup parallel optimization algorithm with the shuffled frog leaping algorithm. Moreover, the developed MPPT technique improves the tracking accuracy of the MPP, increased the speed of MPP tracking, the problem of the traditional frog leaping algorithm being prone to falling into a local optimum is successfully overcame by it. In order to verify the effectiveness of the method, a simulation model is established in MATLAB in this paper. The Simulation results show that the method proposed in this paper can make the photovoltaic power generation system be operated more efficiently, which contributes to the development of the renewable energy field and promotes the progress of clean energy technology. Compared with the traditional algorithm, it has obvious advantages in realizing the MPPT of PV power generation system. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) installations have become integral for harnessing solar energy, yet ensuring uninterrupted power generation remains crucial. This study addresses the challenge of maintaining reliability in PV systems by proposing a method to detect and identify simultaneous faults, using kernel principal component analysis (KPCA) and statistical metrics. The proposed method employs KPCA, a machine learning technique adept at identifying patterns in complex data. By utilizing statistical metrics in a feature space generated by KPCA, potential faults in PV system performance data are flagged. Unlike prior research that focused on single faults, this work extends the application of KPCA to detect and identify multiple faults occurring simultaneously, such as partial shading combined with open or short circuit faults. Through extensive simulations, including 100 samples of different faults under varying irradiance conditions, the method demonstrates high accuracy rates: 93.33% for partial shading, 100% for open circuit, 100% for short circuit, and 81.81% for combinations of partial shading with either open or short circuit faults. Results from a Matlab‐Simulink model validate the effectiveness of KPCA in detecting both single and simultaneous faults in PV systems' DC side. [ABSTRACT FROM AUTHOR]

This article implements maximum power point tracking (MPPT) based on the improved hill-climbing algorithm for photovoltaic (PV) systems feeding resistive loads. A direct current-to-direct current boost converter is inserted between the PV system and the load to achieve matching. The converter is managed using MPPT based on the hill-climbing algorithm. The objective of this paper is to optimize the code program to achieve the best compromise between accuracy and rapidity by implementing this algorithm using a microcontroller. Two PV systems are tested under identical meteorological conditions. In the first, an improved hill-climbing MPPT controller is used whereas, in the second, the conventional version is employed. The experimental results obtained show a significant enhancement in terms of speed for the improved algorithm with a value of 0.4 s for the response time and 3% for the oscillation power; those values remain satisfactory in terms of precision of the algorithm compared with the conventional system studied and the compared algorithm from the literature. [ABSTRACT FROM AUTHOR]

The transition to low‐carbon energy and energy independence of a country play an important role in the sustainable development of its energy sector. Another important issue of sustainable energy development is the cost competitiveness in the generation process; with new renewable energy technologies, a sustainable energy transition to carbon‐neutral society is possible. In this article, we present a view of sustainable energy transformation based on a case study of Malta. We have created a simulation of a Maltese electricity system with projected growth and dominance of photovoltaic energy in the electricity market. The study results suggest that a system with a high penetration of photovoltaics has significant advantage over a conventional system using fossil fuels. In particular, in the simulated Maltese system, the total annual cost of energy was reduced threefold, the CO2 emissions were reduced by 40%, and the energy independence of Malta increased by 60%. In the end, the article gives a recommendation for further research into the Maltese energy system. [ABSTRACT FROM AUTHOR]

Photovoltaic Noise Barriers combine strategies for reducing noise and using renewable energy so that roadsides with low-value lands gain effective functions. The relatively low power density of photovoltaic systems and the projection of increasing pressure on urban lands necessitate further studies to maximize solar panel insolation. The dynamic photovoltaic noise barrier system integrated with solar tracking systems can be an efficient solution to maximize energy extraction from solar panels. This paper focuses on the effect of dynamic photovoltaic noise barrier systems on solar energy and noise control efficiency with different scenarios based on fixed and single-axis tracking technology. Annual optimal fixed tilt of the three cities with different solar characteristics were modeled in the base case scenario (S1), and the tilt angles of six-month, seasonal, and monthly periods were modeled in the S2, S3 and S4 scenarios, respectively. The solar output of alternatives was calculated via PVsyst, and noise control performance was analyzed with SoundPLAN. Energy production and noise control performance are improved by shorter tracking periods and also at locations with high clearness index and low average temperature. Systems adjusted to the S4 scenario maximize both energy and noise control performance, as shown by the results. Ankara showed the highest power production increase compared to S1, with percentages of 3.65%, 3.86%, and 4.46% for the S2, S3, and S4 scenarios, respectively. The maximum noise control performance was observed in Ankara and the rate of exposed receiver points decreased from 88 to 70.1% with the S4 scenario compared to S1. [ABSTRACT FROM AUTHOR]

Recent advancements in power electronics have significantly improved photovoltaic (PV) inverters by equipping them with sophisticated monitoring capabilities. These enhancements provide economic advantages by facilitating swift failure detection and lowering monitoring costs. Educating users on the economic repercussions of undetected failures in specific inverter monitoring systems is crucial. This paper introduces a novel metric, "Cost of Detection", which assesses the financial impact of failures, considering the repair expenses and the "quality" of the monitoring system in place. The study analyzed fifteen inverter monitoring solutions, focusing on the variance in alerts generated by the manufacturers' standard and extra monitoring features. Employing the Failure Mode and Effects Analysis (FMEA) method, alerts were prioritized based on their importance for two PV system scenarios: a low-power residential system (5 kWp) and a medium-power industrial/commercial system (100 kWp). Lisbon, Rome, and Berlin were chosen as the locations for these systems. The economic impact of system failures is evaluated annually for each capacity and city. Given the differing costs and annual yields, comparing their economic performance over time is essential. This comparison utilizes the Net Present Value (NPV), which estimates an investment's worth by calculating the present value of all cash flows. The investment assessment includes only the costs of inverters and optimizers, excluding O&M expenses, licenses, and fees. Over five years, a higher NPV signifies a more economically advantageous solution. For residential systems, string inverters with optimizers have the highest NPV, surpassing those without optimizers by 17% across all three cities. The optimal monitoring solution in the industrial/commercial context was a string inverter with one optimizer for every two panels. Here, Rome emerged as the location with the most substantial NPV increase of 50%, followed by Berlin with 33% and Lisbon with 28%. [ABSTRACT FROM AUTHOR]

This research aims at proposing an alternative to improve the efficiency of electric vehicles (EVs) and reduce greenhouse gas (GHG) emissions in the context of electric mobility. A photovoltaic and wind hybrid energy system was installed in a Chok S2 electric vehicle. In addition, a charge equalization system was included to balance and maximize the performance of each of the EV's five batteries connected in series. The results show a 20% improvement in vehicle efficiency after conducting tests on a 17 km Andean route. The photovoltaic system generated 535 W, while the wind system generated 135 W/s at a speed of 45 km/h. These findings highlight the potential of hybrid renewable energy systems to improve the efficiency and range of electric vehicles. [ABSTRACT FROM AUTHOR]

Solar tracking systems allow an increase in the use of solar energy for its conversion with photovoltaic technology due to the alignment with the sun. However, there is a compromise between tracking accuracy and the energy required to perform the movement action. Consequently, the wear of the tracker components increases, reducing its useful lifetime and affecting the profitability of these systems. The present research develops a novel tracking strategy based on real-time measurements to increase the lifetime without reducing the energy productivity of the tracking systems. The proposed approach is verified experimentally by implementing the real-time decision-making algorithm and a conventional tracking algorithm in identical tracking systems under the same weather conditions. The proposed strategy reduces energy consumption by 14.18 % due to the tracking action, maintaining a practically identical energy generation between both systems. The findings highlight a 53.33 % reduction in the movements required for tracking and a 60.77 % reduction in operation time, which translates into a 6.8 -fold increase in the lifetime of the solar tracking system under the experimental conditions applied. The results are promising, so this research initiates and motivates the development of more complex models to increase the useful life of the tracking systems and their profitability and environmental impact concurrently. [ABSTRACT FROM AUTHOR]

A large-scale integration of photovoltaic (PV) systems can degrade the stability of the power grid. Therefore, it is important to accurately predict the short-term output power generated from PV systems to achieve better grid power distribution and allocation. For this reason, a short-term PV power prediction model that uses the data collected from temperature sensors, irradiance sensors, and other relevant sensors was proposed, in which an improved dragonfly algorithm (IDA) was applied to optimize the support vector machine (SVM). First, the output power curves of PV systems under sunny, cloudy, and rainy conditions were analyzed to determine the input variables of the prediction model, which included temperature, relative humidity, and solar radiation intensity. Second, the original dragonfly algorithm in the optimization process was improved, and then, this IDA was utilized to optimize the parameters of SVM, enhancing the predictive capability of the model. Finally, the IDA-optimized SVM (IDA-SVM) model was applied to predict the PV output power. Test performance results demonstrated that the average absolute percentage errors of IDA-SVM were 2.42, 5.96, and 7.44% for sunny, cloudy, and rainy days, respectively, outperforming other comparative models. The performance results showed that the proposed model can not only improve the stability of PV integration, but also effectively increase the penetration rate of PV energy and enhance the reliability of power system operation. [ABSTRACT FROM AUTHOR]

Environmental concerns and energy security are pressing issues of the 21st century, with a heavy reliance on fossil fuels causing significant environmental pollution and resource depletion. To mitigate these problems, it is crucial to explore and implement alternative clean energy sources. This manuscript proposes a novel crayfish optimization algorithm (COA) for optimal scheduling in a hybrid power system that incorporates various renewable energy sources, like battery energy storage systems (BESS), fuel cells (FC), wind turbines (WT), micro turbines (MT) and photovoltaic (PV) panels. The importance of the work lies in its ability to optimize the entire operating costs of a grid‐connected microgrid while improving the accuracy and efficiency of energy management. The COA method addresses economic dispatch problems and manages energy within the grid‐connected microgrid, accounting for high levels of uncertainty. The proposed approach, tested using MATLAB Simulink, achieved a cost value of 252, outperforming existing methods such as GTO, PSO, SSA, and ALO. This illustrates the potential of the proposed technique to provide more cost‐effective and efficient energy management solutions in hybrid power systems. [ABSTRACT FROM AUTHOR]

Large-scale optimization (LSO) problems among photovoltaic (PV) and concentrated solar power (CSP) systems are attracting increasing attention as they help improve the energy dispatch efficiency of PV and CSP systems to minimize power costs. Therefore, it is necessary and urgent to systematically analyze and summarize various LSO methods to showcase their advantages and disadvantages, ensuring the efficient operation of hybrid energy systems comprising different PV and CSP systems. This paper compares and analyzes the latest LSO methods for PV and CSP systems based on meta-heuristic algorithms (i.e., Particle Swarm Optimization, Genetic Algorithm, Enhanced Gravitational Search Algorithm, and Grey Wolf Optimization), numerical simulation and stochastic optimization methods (i.e., Constraint Programming, Linear Programming, Dynamic Programming Optimization Algorithm, and Derivative-Free Optimization), and machine learning-based AI methods (Double Grid Search Support Vector Machine, Long Short-Term Memory, Kalman Filter, and Random Forest). An in-depth analysis and A comparison of the essence and applications of these algorithms are conducted to explore their characteristics and suitability for PV and CSP or hybrid systems. The research results demonstrate the specificities of different LSO algorithms, providing valuable insights for researchers with diverse interests and guiding the selection of the most appropriate method as the solution algorithm for LSO problems in various PV and CSP systems. This also offers useful references and suggestions for extracting research challenges in LSO problems of PV and CSP systems and proposing corresponding solutions to guide future research development. [ABSTRACT FROM AUTHOR]

Simple Summary: Aquaculture is presented as a viable solution to overfishing and the growing demand for marine products. This study investigates the rearing of rainbow trout (Oncorhynchus mykiss) in a recirculating system at 3000 m above sea level, in Copaquilla, northern Chile. A total of 5000 juvenile trout were transported from the Rio Blanco fish farm over 2100 km. For 20 months, growth parameters were evaluated, including specific growth rate, weight gain, feed conversion, survival, and Fulton's condition factor. The results indicated normal growth and good quality, confirming the feasibility of trout aquaculture at a high altitude. This suggests new opportunities for aquaculture in the Andean region of northern Chile. Given the overexploitation of fisheries and the growing consumption of sea products, aquaculture is emerging as an alternative to meet the demand for protein at regional, national, and global levels. In northern Chile, the foothills of the Andes offer an opportunity for sustainable economic diversification. In this study, results of a rainbow trout (Oncorhynchus mykiss) culture in a recirculation system are presented, analyzing its growth and performance under altitude conditions. The research was carried out in Copaquilla, a small area in the foothills of northern Chile, 3000 m above sea level. Five thousand 15 g juvenile trout were acquired and transported by land from the Rio Blanco fish farm, successfully traveling more than 2100 km. During the 20-month-long culture, several growth parameters were evaluated, including specific growth rate, percentage of weight growth, feed conversion factor, survival, and Fulton's condition factor. All these parameters were within the normal growth range for trout, evidencing good-quality growth of the organisms. The results offer evidence that rainbow trout farming at 3000 m above sea level is viable and presents a viable performance, opening new opportunities for aquaculture in northern Chile. [ABSTRACT FROM AUTHOR]

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents the development and execution of a Single-Ended Primary-Inductor Converter (SEPIC) multiplier within a Hardware-in-the-Loop (HIL) emulation environment tailored for photovoltaic (PV) applications. Utilizing the advanced capabilities of the dSPACE 1104 platform, this work establishes a dynamic data exchange mechanism between a variable voltage power supply and the SEPIC multiplier converter, enhancing the efficiency of solar energy harnessing. The proposed emulation model was crafted to simulate real-world solar energy capture, facilitating the evaluation of control strategies under laboratory conditions. By emulating realistic operational scenarios, this approach significantly accelerates the innovation cycle for PV system technologies, enabling faster validation and refinement of emerging solutions. The SEPIC multiplier converter is a new topology based on the traditional SEPIC with the capability of producing a larger output voltage in a scalable manner. This initiative sets a new benchmark for conducting PV system research, offering a blend of precision and flexibility in testing supervisory strategies, thereby streamlining the path toward technological advancements in solar energy utilization. [ABSTRACT FROM AUTHOR]

Currently, approximately 40% of the energy demand in the European Union (EU) is used in buildings and the energy demand for cooling and heating is increasing every year, so the energy efficiency of buildings represents a problem of increased interest and of extreme relevance. The article proposes, through the case study, the installation of a photovoltaic system for the production of electricity on the roofs of the buildings and the cooling and heating of the building using a water-to-water heat pump. Having the advantage of the location of the studied buildings on the banks of the river Bega in the town of Timisoara, the case study proposes the cooling of the spaces in the buildings in two variants, namely in the spring and autumn when the outside temperature is not very high using the river water directly ("free cooling"), and in the summer for the hot months, a water-to-water heat pump to cool the cooling agent to the parameters necessary to achieve a proper air conditioning. [ABSTRACT FROM AUTHOR]

In this article, a model predictive control (MPC) with common-mode voltage (CMV) suppression is proposed for single-phase cascaded H-bridge (CHB) inverters, which can also simultaneously achieve control objectives of gridconnected current tracking, voltages balancing of different H-bridge submodules on the DC-side and switching frequency reduction. To suppress high-frequency components of the common-mode voltage without additional switching devices, the algorithm proposed designs the predicted and reference values of the CMV and incorporates them in the cost function. At the same time, the capacitor voltages balancing control is integrated in the calculation of the optimal modulation function of the H-bridge, which reduces the complexity of control effectively. Besides, switching times of the MOSFETs are compared in two cycles. The cost function is constructed to represent comprehensive effect of the control. Finally, an experiment is performed on the hardware-in-the-loop experimental platform. The experimental results show that the proposed algorithm can offer a better voltage THD and reduce the times of switch action by nearly half while maintaining high-precision current tracking and maximum power point of photovoltaic modules, which alleviate the potential electromagnetic interference and cabling problem. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic approach to enhance the performance of a 21‐level asymmetrical multilevel inverter (MLI) with less power electronics switches and DC voltage sources. In the first step (Configuration 1), the voltage level is improved at the cost of DC‐link voltage utilization. In the second step (Configuration 2), the ratio of the DC‐link voltages is modified to improve the utilization of DC‐link voltage, with an insignificant reduction of voltage levels as compared to Configuration 1. Finally, another modification of the MLI configuration is proposed (Configuration 3), by incorporating an H‐bridge in place of a T‐type module for further improvement of DC source utilization and increasing the number of voltage levels. Thus, Configuration 3 improves the inverter's total standing voltage (TSV) and efficiency. To reduce the number of DC sources, the output voltage levels are obtained in Configuration 3 by adding and subtracting the input DC sources. Moreover, conducting switches are reduced to minimize conduction loss and maximize efficiency. The DS1103‐based digital controller is used to verify the performance of the proposed configurations, which are compared with the literature‐based MLI models. [ABSTRACT FROM AUTHOR]

Renewable energy communities foster the users' engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects. [ABSTRACT FROM AUTHOR]

This study presents a new approach for Maximum Power Point Tracking (MPPT) by combining the honey badger algorithm (HBA) with a Genetic Algorithm (GA). The integration aims to optimize photovoltaic (PV) system performance in partial shading conditions (PSCs). Initially, the HBA is utilized to explore extensively and identify potential solutions while avoiding local optima. If necessary, the GA is then employed to escape local optima through selection, crossover, and mutation operations. On average, this proposed method has a 40% improvement in tracking time and 0.77% in efficiency compared with the HBA. In a dynamic case, the proposed method achieves a 4.81% improvement compared to HBA. [ABSTRACT FROM AUTHOR]

In recent years, grid-connected multifunctional photovoltaic (PV) systems have proven to be highly efficient. This system integrates PV panels with a DC–DC boost converter (DC–DC–BC) and the electrical distribution grid (DEG). Linking the PV to the AC-DEG is accomplished through a three-level multifunctional voltage source inverter (MVSI). The DC–DC–BC component in this study is engineered to perform maximum power point tracking (MPPT) irrespective of normal or abnormal conditions. The conventional MPPT technique poses several challenges and constraints on system usage. Hence, the suggestion is to adopt synergetic control (SC) and sliding mode control (SMC) to enhance the MPPT technique's performance within the proposed system framework. Moreover, predictive direct power control is applied to the MVSI-based shunt active power filter, utilizing a phase-locked loop technique to achieve multiple objectives: minimizing energy fluctuations, injecting active power, correcting power factors, compensating reactive power, and mitigating harmonic currents. To implement the proposed system, the MATLAB is used for this purpose, with several tests used to study the behavior of the controls proposed in this work. Numerical results indicate significant reductions in active and reactive power fluctuations, with estimated rates of 38.46% and 15.30%, respectively, compared to traditional strategies. Moreover, the total harmonic distortion (THD) of the source current after filtering is reduced by 31.88% under solar irradiation of G = 1000 Wm2. Before filtering, the THD of current experiences a reduction estimated at 97.65%. These findings underscore the superior performance of the proposed control technique across all evaluated aspects. [ABSTRACT FROM AUTHOR]

Background: PV/diesel microgrids are getting more popular in rural areas of sub-Saharan Africa, where the national grid is often unavailable. Most of the time, for economic purposes, these hybrid PV/diesel power plants in rural areas do not include any storage system. This is the case in the Bilgo village in Burkina Faso, where a PV/diesel microgrid without any battery storage system has been set up. This power plant is composed of three diesel generators operating in parallel (two of 16 kW and one of 24 kW), coupled with a photovoltaic field of 30 kWp. It was observed that for such power plants, the grid management is not always efficient due to constantly fluctuating solar output and loads. This inconsistency in energy output raises the question if integrating battery energy storage systems could improve the grid's performance. While many studies in the literature focus on hybrid energy systems, only a few of them have tackled the optimization of existing and operational systems. Methods: This study investigated three scenarios based on the existing microgrid's characteristics: conventional standalone diesel generators, PV/diesel without battery storage and PV/diesel with a battery storage system which are the main technologies used for off-grid rural electrification in Burkina Faso. The levelized cost of electricity (LCOE) was used to assess the economic performance of each scenario, and the calculations were made using the HOMER software. Results: It was found that the best among the scenarios considered is the PV/diesel/battery configuration which has the lowest LCOE of US$ 0.524/kWh. The battery storage system for the optimal configuration has a capacity of 182 kWh with about 8 h of autonomy. Conclusions: It can be inferred from this study that a storage unit is necessary for an optimal management of a PV/diesel microgrid. Indeed, the storage unit significantly reduces the operating and maintenance costs associated with running diesel generators, as well as the excess electricity. The storage system also allows for a greater reduction in CO2 emissions compared to systems without storage. [ABSTRACT FROM AUTHOR]

In this study, a photovoltaic system with an installed power of 10 kWp, which was built on the roof of a school cafeteria in Gümüşhacıköy district of Amasya, was modeled using the PVsyst software and PVSOL software. The real-life data of the system and the production data calculated from the software used were compared. While the actual total electricity production data of this photovoltaic system for 2021 was determined as 12.473 MWh, this value was obtained as 12.912 MWh using PVsyst software and 13.556 MWh using PVSOL software. While there was a 3.40% difference between the estimated production value and the real-life data with the PVsyst software, it was determined that there was a 7.99% difference between the estimated production value and the actual production value of the system as a result of the simulation made with PVSOL. From the production estimates made with two different simulation software, it was seen that the PVsyst software gave results closer to the real-life data. [ABSTRACT FROM AUTHOR]

The global shift toward renewable energy sources in response to the depletion of fossil fuel reserves and environmental concerns has spotlighted photovoltaic (PV) systems as key players in sustainable power generation. This research presents a novel approach aimed at maximizing PV module power generation and seamlessly integrating it with the electrical grid. The innovation lies in the development of a clamped Z-source boost converter, enhancing PV voltage to facilitate efficient power transfer to the grid. Additionally, a Multi-Objective Genetic Algorithm Optimized Radial Basis Function Neural Network is proposed for continuous Maximum Power Point Tracking, ensuring optimal power extraction and enhanced converter performance. A 1Φ voltage source inverter with a proportional integral controller regulates power injection into the grid while ensuring synchronization. The study's outcomes, validated through a combination of FPGA controller and MATLAB simulations, showcase real-time control and high-speed processing by the FPGA, coupled with comprehensive analysis and optimization through MATLAB simulations. The proposed converter achieves an impressive 96.8% efficiency and maximizes the voltage gain ratio. This research contributes significantly to the field by providing a cutting-edge solution for efficient PV power integration with the grid, addressing key challenges in renewable energy systems. [ABSTRACT FROM AUTHOR]

The solar photovoltaic system suffers from varied environmental condition and causes power losses. This paper presents a Modified Artificial Rabbit Optimization based algorithm for MPPT. The performance of the Artificial Rabbit Optimization algorithm is improved by replacing the hidden parameter in the random hiding stage with levy flight strategy. The performance of the proposed algorithm is compared with particle swarm optimization, slap swarm optimization and artificial rabbit optimization under varying environmental conditions like change in uniform irradiance to partial shaded condition to uneven shaded condition and load variation. The simulation and experimental result gives a promising result by outperforming the particle swarm optimization, slap swarm optimization and artificial rabbit optimization algorithm in terms of quick convergence and decreasing oscillation towards maximum power points. [ABSTRACT FROM AUTHOR]

In recent years, extensive and expensive research has been conducted on solar energy systems. Studying and investigating solar panel output requires considerable experimental work, increasing both time and costs. This research aims to reduce these by integrating fuzzy logic with Response Surface Methodology (RSM). In fuzzy models, data inputs are processed through membership functions and rules based on expert knowledge or assumptions. These rules generate outputs, which are then defuzzified into actionable decisions. These outputs were used as inputs in the RSM to develop a statistical prediction model. The model developed is based on three inputs: light intensity, temperature, and humidity, with one output: power. The fuzzy model was processed assuming two levels for humidity, temperature, and light intensity. The RSM was designed using data extracted from the fuzzy system for seventeen runs, using the Box-Behnken Design (BBD) as part of the RSM with Design-Expert software. The advantage of using BBD is that it avoids extreme corners in the design. The results were analyzed using Analysis of Variance (ANOVA). The ANOVA table showed significant results for the quadratic regression model. The results were compared with real data using random samples of twenty readings each for two-time intervals. The validation showed variations averaging 7.50% and 5.53%. [ABSTRACT FROM AUTHOR]

Autonomous photovoltaic (PV) street lighting systems, most suitable for use in areas distant from power grids, have traditionally been constructed according to the modular principle, with each lighting point equipped with its own PV panel (PVP) and battery. Analysis indicates that such implementation with distributed generation and storage of electrical energy faces challenges in matching installed PVP power and battery capacity with seasonal changes in insolation and daily lighting duration, particularly in middle and northern latitudes. To address this inconsistency, this work proposes a hybrid configuration for autonomous street lighting systems, concentrating generation and storage of electrical energy in one designated location. Apart from facilitating equipment maintenance, this configuration allows for a reduction in installed PVP power and battery capacity. It also addresses electricity shortages during winter days by incorporating an additional energy source, such as a gasoline electricity-generating set. This paper examines the viability of such a solution using park lighting as an example, within the climatic conditions of the Western region of Ukraine. Optimization of the parameters of the main constituent elements of the proposed autonomous park lighting system, with concentrated generation and storage of electrical energy, was carried out to minimize annual costs. For the optimal configuration, costs were found to be 47% lower compared to traditional autonomous systems with distributed generation and storage of electrical energy. [ABSTRACT FROM AUTHOR]

Considering environmental concerns, electric vehicles (EVs) are gaining popularity over conventional internal combustion (IC) engine-based vehicles. Hybrid energy-storage systems (HESSs), comprising a combination of batteries and supercapacitors (SCs), are increasingly utilized in EVs. Such HESS-equipped EVs typically outperform standard electric vehicles. However, the effective management of power sources to meet varying power demands remains a major challenge in the hybrid electric vehicles. This study presents the development of a MATLAB Simulink model for a hybrid energy-storage system aimed at alleviating the load on batteries during periods of high power demand. Two parallel combinations are investigated: one integrating the battery with a supercapacitor and the other with a photovoltaic (PV) system. These configurations address challenges encountered in EVs, such as power fluctuations and battery longevity issues. Although batteries are commonly used in conjunction with solar PV systems for energy storage, they incur higher operating costs due to the necessity of converters. The findings suggest that the proposed supercapacitor–battery configuration reduces battery peak power consumption by up to 39%. Consequently, the supercapacitor–battery HESS emerges as a superior option, possibly prolonging battery cycle life by mitigating stress induced by fluctuating power exchanges during the charging and discharging phases. [ABSTRACT FROM AUTHOR]

The paper presents a normalized, varying step size-based least mean square-based control for a standalone microgrid and Ant Lion optimization-based maximum power point tracking. This modified version of the incremental conductance algorithm addresses issues like slow dynamic response, fixed step size issues, and steady state oscillations. Comparative analysis with well-known techniques shows that Ant Lion optimizes the tracking of maximum power points more accurately, with fewer oscillations and increased efficiency. Power management and power quality are maintained through proposed adaptive voltage source converter control. The proposed voltage control shows better performance under various dynamic and steady conditions. [ABSTRACT FROM AUTHOR]

This paper presents an improved rule-based control scheme for battery energy storage (BES) system with the goal of minimising the fluctuation output from photovoltaic (PV) sources while ensuring the operational constraints of BES are regulated at the specified ranges for safety purposes. The control scheme is formulated in accordance with the intended operational limitations of the BES, including charge/discharge current limits and state-of-charge (SOC). The simulation studies were carried out using MATLAB/Simulink to evaluate the effectiveness of an improved rule-based control scheme on the 1.2 MW PV system data acquired from a location in Malaysia. Furthermore, a comparative study on the effectiveness of an improved rule-based control scheme compared with the conventional rule-based control scheme has been carried out. The simulation results show that an improved rule-based control scheme can effectively reduce the fluctuations in the output power of the PV sources and dispatch the output to the utility grid on an hourly basis with an efficiency of up to 94.47%. Finally, the comparison results on the effects of the BESS capacity also illustrate that an improved rule-based control scheme is more effective compared to the conventional rule-based control scheme in the previous study. [ABSTRACT FROM AUTHOR]

Partial shading (PS) is a common issue in photovoltaic systems (PVs), and it can significantly reduce the system's output power. This paper presents the advanced particle swarm optimization (APSO) algorithm. APSO is designed to alleviate the challenges posed by PS in PVs in from where of effectiveness and stability speed so that it works to achieve and maintain the global maximum power point (GMPP) under PS conditions. It leverages persistent variables to store and track system states and iterations; it also includes checks to ensure that the duty cycle remains within specified bounds facilitating more effective optimization. Additionally, APSO optimizes solar panel duty cycles and velocities to converge toward an optimal solution to improve overall power generation efficiency and settling time. The results evaluation involves testing the performance of photovoltaic panels under three different shading scenarios and comparative analysis against recent Heuristic-optimization-based GMPP techniques, this study and comparative analyses demonstrate APSO's effectiveness and superiority in terms of high efficiency that reaches 99.85% and fast settling time of GMPP at less than 0.01 second across all test cases. APSO presents a promising solution for maximizing PV power output in the presence of partial shading. [ABSTRACT FROM AUTHOR]

Abstract Nowadays environmental concerns and fossil fuel limitations, as well as economic benefits and technical issues such as reliability lead conventional distribution networks to smart microgrids, where the renewable energy resources are merged with the grids. Nevertheless, the systems face new challenges due to the intermittent nature and uncertainties in the distributed generations, which cause frequency fluctuations in the microgrid. The photovoltaic cells are the main part of the contemporary microgrids. Although the photovoltaic (PV) systems depend on solar irradiance, and temperature and are affected by the partial shading phenomenon they could contribute to improving the microgrid frequency stability with a proper control scheme. In this paper, the frequency control strategy is designed for a hybrid stand-alone microgrid, which is robust against load disturbances, variations in weather conditions, and uncertainties in the microgrid parameters. The proposed intelligent control scheme relies on the Recurrent Adaptive Neuro Fuzzy Inference System (RANFIS). The Whale Optimization Algorithm (WOA) is employed to optimize the RANFIS controller structure and generate the parameters of membership functions. In this multi-objective optimization, the objectives are settling time (ST), overshoot (Osh), and Integral Square Error (ISE). The simulation results verify the high robustness and performance of the proposed RANFIS controller, compared to other controllers, during various operational circumstances, as well as the sporadic behavior of renewable energy resources (RES) such as fluctuations in solar radiation and certain uncertainties in the microgrid parameters.

Globalization has led to a rapid rise in energy consumption, making climate change one of the world's most pressing issues. As wastewater treatment plants (WWTPs) contribute to climate change by emitting greenhouse gases (GHGs), this study estimated the total GHG emissions of WWTPs by classifying them as either direct or indirect carbon emissions. The effectiveness of the use of solar photovoltaic systems and biogas produced by WWTPs in increasing energy recovery and reducing GHG emissions was investigated. This study demonstrated that the use of an up-flow anaerobic sludge blanket (UASB) reactor with a biogas flow of 9 120.77 m3/d and an activated sludge processing system (ASPS) reactor with a biogas flow of 14 004 m3/d, in addition to the energy production from the UASB reactor (6 421.8 MW⸱h per year) and the ASPS reactor (9 860.0 MW⸱h per year), yielded a reduction of 3 316.85 and 5 092.69 t of CO2 equivalent per year, respectively. Furthermore, the co-design of wastewater processes could be utilized to optimize biogas energy recovery. Moreover, the use of solar photovoltaic systems reduced GHG emissions from WWTPs. This is important to the transition to renewable energy because it resulted in a 10%–40% reduction in carbon emissions from WWTPs. Integrating renewable energy sources, biogas, and solar energy could provide up to 88% of the annual energy requirements of WWTPs. Recommendations are provided for further research considering the limited availability of integrated resources for studying the simultaneous utilization of photovoltaic and biogas systems.

Abstract In recent years, grid-connected multifunctional photovoltaic (PV) systems have proven to be highly efficient. This system integrates PV panels with a DC–DC boost converter (DC–DC–BC) and the electrical distribution grid (DEG). Linking the PV to the AC-DEG is accomplished through a three-level multifunctional voltage source inverter (MVSI). The DC–DC–BC component in this study is engineered to perform maximum power point tracking (MPPT) irrespective of normal or abnormal conditions. The conventional MPPT technique poses several challenges and constraints on system usage. Hence, the suggestion is to adopt synergetic control (SC) and sliding mode control (SMC) to enhance the MPPT technique's performance within the proposed system framework. Moreover, predictive direct power control is applied to the MVSI-based shunt active power filter, utilizing a phase-locked loop technique to achieve multiple objectives: minimizing energy fluctuations, injecting active power, correcting power factors, compensating reactive power, and mitigating harmonic currents. To implement the proposed system, the MATLAB is used for this purpose, with several tests used to study the behavior of the controls proposed in this work. Numerical results indicate significant reductions in active and reactive power fluctuations, with estimated rates of 38.46% and 15.30%, respectively, compared to traditional strategies. Moreover, the total harmonic distortion (THD) of the source current after filtering is reduced by 31.88% under solar irradiation of G = 1000 Wm2. Before filtering, the THD of current experiences a reduction estimated at 97.65%. These findings underscore the superior performance of the proposed control technique across all evaluated aspects.

Abstract Background PV/diesel microgrids are getting more popular in rural areas of sub-Saharan Africa, where the national grid is often unavailable. Most of the time, for economic purposes, these hybrid PV/diesel power plants in rural areas do not include any storage system. This is the case in the Bilgo village in Burkina Faso, where a PV/diesel microgrid without any battery storage system has been set up. This power plant is composed of three diesel generators operating in parallel (two of 16 kW and one of 24 kW), coupled with a photovoltaic field of 30 kWp. It was observed that for such power plants, the grid management is not always efficient due to constantly fluctuating solar output and loads. This inconsistency in energy output raises the question if integrating battery energy storage systems could improve the grid's performance. While many studies in the literature focus on hybrid energy systems, only a few of them have tackled the optimization of existing and operational systems. Methods This study investigated three scenarios based on the existing microgrid's characteristics: conventional standalone diesel generators, PV/diesel without battery storage and PV/diesel with a battery storage system which are the main technologies used for off-grid rural electrification in Burkina Faso. The levelized cost of electricity (LCOE) was used to assess the economic performance of each scenario, and the calculations were made using the HOMER software. Results It was found that the best among the scenarios considered is the PV/diesel/battery configuration which has the lowest LCOE of US$ 0.524/kWh. The battery storage system for the optimal configuration has a capacity of 182 kWh with about 8 h of autonomy. Conclusions It can be inferred from this study that a storage unit is necessary for an optimal management of a PV/diesel microgrid. Indeed, the storage unit significantly reduces the operating and maintenance costs associated with running diesel generators, as well as the excess electricity. The storage system also allows for a greater reduction in CO2 emissions compared to systems without storage.

Introduction. Recently, photovoltaic (PV) systems are increasingly favored for converting solar energy into electricity. PV power systems have successfully evolved from small, standalone installations to large-scale, grid-connected systems. When the nonlinear loads are connected to a grid-tied PV system, the power quality can deteriorate due to the active power supplied by the PV array, there’s a noticeable decline in the quality of power delivered to consumers. Its combination with the shunt active power filter (SAPF) enhances system efficiency. Consequently, this integrated system is adept at not only powering local loads but also at compensating for reactive power and filtering out harmonic currents from the main grid. The novelty of the work describes how an operation of a small scale PV system connected to the low voltage distribution system, and nonlinear load can be achieved, the investigation aims to analyze the system’s behavior and elucidate the advantages of employing various control algorithms. These proposed algorithms are designed to ensure a unity power factor for the utility grid while prioritizing high convergence speed and robustness against load power fluctuations across different levels of solar irradiation affecting the PV modules. The purpose of this work is to enhance the dynamic performance of the SAPF by cooperatively using a self-tuning filter (STF) based instantaneous active and reactive power method (PQ) with a novel predictive current control, enhance the system resilience, ensure optimal management of the total active power between the PV system, the electrical network and the non-linear load by integrating the functionalities of the SAPF under different levels of solar irradiation and maintain the DC-link capacitor voltage constant. Methods. A novel predictive current controller is designed to generate the switching signals piloted the three phase source voltage inverter, also a novel algorithm of instantaneous active and reactive power is developed, based on STF, to extract accurately the harmonic reference under non ideal grid voltage, also the perturb and observe algorithm is used to extract, under step change of solar irradiation, the maximum power point tracking of the PV module and the PI controller is used to maintain constant the DC-link capacitor voltage of the SAPF. Results. The efficacy of the proposed system is primarily centered on the grid side, and the performance evaluation of the control system is conducted using the STF based PQ algorithm and predictive current control. In addition, comprehensive testing encompasses all modes of operation, including scenarios involving distorted voltage sources, step changes in solar radiation, and variations in nonlinear loads. Results highlight superior performance in both transient and stable states, affirming the robustness and effectiveness of the proposed controllers. Practical value. The total harmonic distortion value of the grid current for all tests respects the IEEE Standard 519-1992. [ABSTRACT FROM AUTHOR]

This article presents the development and execution of a Single-Ended Primary-Inductor Converter (SEPIC) multiplier within a Hardware-in-the-Loop (HIL) emulation environment tailored for photovoltaic (PV) applications. Utilizing the advanced capabilities of the dSPACE 1104 platform, this work establishes a dynamic data exchange mechanism between a variable voltage power supply and the SEPIC multiplier converter, enhancing the efficiency of solar energy harnessing. The proposed emulation model was crafted to simulate real-world solar energy capture, facilitating the evaluation of control strategies under laboratory conditions. By emulating realistic operational scenarios, this approach significantly accelerates the innovation cycle for PV system technologies, enabling faster validation and refinement of emerging solutions. The SEPIC multiplier converter is a new topology based on the traditional SEPIC with the capability of producing a larger output voltage in a scalable manner. This initiative sets a new benchmark for conducting PV system research, offering a blend of precision and flexibility in testing supervisory strategies, thereby streamlining the path toward technological advancements in solar energy utilization.

[Introduction] With the advancement of the "dual carbon" goals and the introduction of new energy allocation and storage policies in various regions, there is a need to further clarify the role of distributed energy storage in the new types of distribution networks and the configuration of associated energy storage system. [Method] This paper began by summarizing the configuration requirements of the distributed energy storage systems for the new distribution networks, and further considered the structure of distributed photovoltaic energy storage system according to different application needs. To maximize the economic aspect of configuring energy storage, in conjunction with the policy requirements for energy allocation and storage in various regions, the paper clarified the methods for configuring distributed energy storage systems and summarized the commonly used algorithms for determining the location and capacity. Based on this, research suggestions were proposed. [Result] Proper configuration of energy storage should be based on clear demands, selecting the appropriate topology and offering a configuration plan that is optimized by comprehensively considering indicators such as power supply stability, security, and economic efficiency of the distribution network. [Conclusion] Distributed energy storage technology is the key aspect of the new distribution networks and an essential means to ensure the safe and stable operation of distribution networks. To harness its full potential, further research into its optimal configuration and related control technologies is necessary.

At present, new energy is introduced into the power system in large quantities. In order to make it replace the traditional hydropower and gas units as the black start power supply, segmentation restoration strategy of power grid based on the combined optical storage system is studied. Firstly, according to the structure and control mode of the optical storage system, the energy storage system establishes a stable grid-connected bus voltage is proposed, and the photovoltaic system is connected to the grid step by step. Secondly, in order to enhance the damping inertial support of photovoltaic power generation system, virtual synchronous control is proposed to be added to the inverter side of photovoltaic system. Finally, in the process of grid connection between the units to be started and the system load, the energy storage system is configured with additional damping controller to suppress the sub-synchronous oscillation generated by the grid black start, so as to ensure the stable recovery of the grid. In PSCAD/EMTDC, the electromagnetic transient simulation model of black start of the combined optical storage system is built and the simulation analysis is carried out. The results show that the busbar voltage deviation and system frequency deviation in the recovery process of the system are less than 0.65% and less than 1%, which meets the requirements of black start and verifies the feasibility of the start-up strategy proposed in the paper.

Introduction. With the increasing complexity of power systems and the integration of diverse energy sources, issues such as voltage sags, swells, and signal distortions have emerged as critical challenges. These power quality problems can result in equipment malfunction, production downtime, and financial losses for industries, as well as inconvenience and potential damage to electrical appliances in households. There is an urgent need for enhanced system efficiency. Methods. This objective is effectively achieved through the utilization of the newly proposed power theory, which is rooted in solar photovoltaic (PV) control, in conjunction with the Unified Power Quality Conditioner (UPQC). Purpose. The proposed method incorporates a modified synchronous reference frame scheme, coupled with a phase-locked loop mechanism. This control strategy enables the UPQC to effectively mitigate power quality issues. Novelty. PV-UPQC is utilized to uphold power integrity in the presence of diverse current and voltage distortions. This device, known as a multi-objective power conditioning apparatus, serves the purpose of maintaining power quality. PV-UPQC incorporates both a shunt and series voltage source converter, which are interconnected through a shared DC-link. Additionally, the PV system is interconnected at the DC-link of the UPQC in order to supply power to the load. Results. In this study, a novel approach is presented for controlling the UPQC, aiming to address power quality concerns such as unbalanced grid voltage and harmonic distortions and enabling us to control active and reactive power. References 16, tables 2, figures 15.

This paper proposes a new technique for a single-direction solar tracker. The proposed design is based on a sun sensor system that controls the position of the solar panel. The sun sensors of the proposed design contain four photodiodes that are placed on the solar panel in specific angles and directions. The proposed system has several advantages such as the simplicity of implementing the system. This system combines the real-time tracking of sunlight and the low cost of applying a single-direction tracker. The prototyping experiment and Simulink MATLAB were applied to show the advantages of applying a single-direction tracker by following the angle of sunlight during the day. Real-time sun position and irradiation data were applied. The experimental results show that the proposed single-axis sun sensor PV tracker system generates around 20 more electric power than a fixed-structure PV system. [ABSTRACT FROM AUTHOR]

The primary disadvantage of solar photovoltaic systems, particularly in partial shadowing conditions (PSC), is their low efficiency. A power–voltage curve with a homogenous distribution of solar irradiation often has a single maximum power point (MPP). Without a doubt, it can be extracted using any conventional tracker—for instance, perturb and observe. On the other hand, under PSC, the situation is entirely different since, depending on the number of distinct solar irradiation levels, the power–voltage curve has numerous MPPs (i.e., multiple local points and one global point). Conventional MPPTs can only extract the first point since they are unable to distinguish between local and global MPP. Thus, to track the global MPP, an optimized MPPT based on optimization algorithms is needed. The majority of global MPPT techniques seen in the literature call for sensors for voltage and current in addition to, occasionally, temperature and/or solar irradiance, which raises the cost of the system. Therefore, a single-sensor global MPPT based on the recent red-tailed hawk (RTH) algorithm for a PV system interconnected with a DC link operating under PSC is presented. Reducing the number of sensors leads to a decrease in the cost of a controller. To prove the superiority of the RTH, the results are compared with several metaheuristic algorithms. Three shading scenarios are considered, with the idea of changing the shading scenario to change the location of the global MPP to measure the consistency of the algorithms. The results verified the effectiveness of the suggested global MPPT based on the RTH in precisely capturing the global MPP compared with other methods. As an example, for the first shading situation, the mean PV power values varied between 6835.63 W and 5925.58 W. The RTH reaches the highest PV power of 6835.63 W flowing through particle swarm optimization (6808.64 W), whereas greylag goose optimizer achieved the smallest PV power production of 5925.58 W. [ABSTRACT FROM AUTHOR]

The cascaded H-bridge (CHB) inverter has become pivotal in grid-connected photovoltaic (PV) systems owing to its numerous benefits. Typically, DC–DC converters are employed to boost the input voltage in grid-connected systems to meet the grid's higher voltage requirements, but this approach increases equipment size and cost. To enhance inverter efficiency, this paper proposes a boost-type, three-phase CHB PV grid-connected inverter. This design can raise the input voltage and satisfy grid requirements with only a few additional components. Additionally, PV environmental fluctuations can cause variations in PV power generation, leading to a power imbalance in the inverter and potentially affecting the stability of the PV system. Based on this, we consider grid voltage fluctuations induced by unbalanced power output from the inverter and propose an improved control method based on the superposition of zero-sequence components. Finally, we construct a simulation model and conduct experimental verification using the MATLAB/Simulink platform. The validation results demonstrate that this topology reduces equipment volume and effectively enhances the efficiency of PV power generation systems. Furthermore, the designed control method ensures system stability while effectively mitigating power imbalances caused by PV module and grid voltage fluctuations. [ABSTRACT FROM AUTHOR]

Photovoltaic solar energy is one of the renewable energy options that allows us to reduce the effects of climate change. Furthermore, it is an inexhaustible source available on the planet that contributes to sustainable development. This research adopts both a qualitative and quantitative approach, with global irradiance data having been collected during the years 2012-2021, obtaining an average of 4.19788030 W/m² along with peak solar hours, which resulted in variations in the results. Subsequently, the number of photovoltaic solar panels, the inverter, the batteries and the controller required were calculated. The scheme of a photovoltaic module for a water pumping system for crops was also designed, using the Wondershare EdrawMax software. In addition, a survey was conducted among farmers to determine the daily consumption and power of the water pump they use to irrigate crops. [ABSTRACT FROM AUTHOR]

Due to the environmental damage of fossil fuels and being an exhaustible resource, the need for high efficiency has emerged as a result of the increasing demand in the renewable energy branch. The need for some performance analyzes used to determine the efficiency level before the installation of power plants has also increased in the same direction. By means of the software developed in this direction, data such as estimated production values and amortization period of the power plant can be accessed in advance before the installation of the power plant is started. With these software, investors determine the areas with the highest efficiency and start the plant installation. In this study, the Lüytob production data of a photovoltaic system installed in the Gençali village of Dinar town of Afyonkarahisar with a capacity of 1.166 kWp/1000kWe producing electrical energy were compared with the production values of the PV*SOL program. In this study, the actual production results were compared with the PV*SOL program and the reliability of the results of the PV*SOL program was evaluated. [ABSTRACT FROM AUTHOR]

Due to the intermittent and unstable nature of PV systems, maximum power point tracking (MPPT) of PV systems is necessary in practice. At the same time, PV cells are also shaded by objects such as trees and houses in the environment causing local shadows, so the Lévy flight particle swarm optimisation (LFPSO) is proposed in this paper. The random walk process of Lévy flight is added to the particle swarm optimisation (PSO) to increase the diversity of the search, which can avoid falling into local optimal solutions. The experimental simulation results show that the algorithm proposed in this paper can still accurately track the global maximum power in the case of partial shading, which well avoids falling into the local maximum power. The performance is much higher than the traditional maximum power point tracking algorithm, which improves the efficiency of the PV system. [ABSTRACT FROM AUTHOR]